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字符串核
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  string kernel
     This paper delivers a method using string kernel in support vector machine without word segmentation and the experiment reports a good result.
     论文尝试一种基于字符串核函数的支持矢量机方法来避开分词对中文文本分类,实验表明此方法表现出较好的分类性能。
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     Decommission of nuclear faclities
     设施退役
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     NUCLEON STRUCTURE
     子结构
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     Research of String Matching Techniques
     字符串匹配技术研究
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     Packed Character String Sorting
     紧缩字符串排序法
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  string kernel
Using string kernel to predict signal peptide cleavage site based on subsite coupling model
      
Using string kernel to predict signal peptide cleavage site based on subsite coupling model
      
After the data-preprocessing, the string kernel-based SVM is trained on the HPV sequence data set and tested on the unknown sequences.
      
A survey of related string kernel work is given in the longer version of this paper.
      
But unlike the SW alignment, it has been proven that it is a valid string kernel.
      
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Text Categorization is the first step to gain information from textual data,existing methods are mainly based on statistical method or machine learning,such as Bayes,KNN,SVM,Neural Network,which have proved to be accurate and stable in experiments for categorizing English texts.However,Chinese text categorization is much more difficult because there is no space between words,and word segmentation has always been used to solve this problem.This paper delivers a method using string kernel in support vector machine...

Text Categorization is the first step to gain information from textual data,existing methods are mainly based on statistical method or machine learning,such as Bayes,KNN,SVM,Neural Network,which have proved to be accurate and stable in experiments for categorizing English texts.However,Chinese text categorization is much more difficult because there is no space between words,and word segmentation has always been used to solve this problem.This paper delivers a method using string kernel in support vector machine without word segmentation and the experiment reports a good result.

文本分类是获取文本信息的重要一步,现有的分类方法主要是基于统计理论和机器学习的,其中著名的有Bayes[1]、KNN[2]、SVM[3]、神经网络等方法。实验证明这些方法对英文分类都表现出较好的准确性和稳定性[4]。对于中文文本分类,涉及对文本进行分词的工作。但是中文分词本身又是一件困难的事情[5]。论文尝试一种基于字符串核函数的支持矢量机方法来避开分词对中文文本分类,实验表明此方法表现出较好的分类性能。

 
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